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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Ingestion and Data Preparation15%-20%- Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
  • 1. Create tables and views
  • 2. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
  • 3. Find external data sets that correlate with available data
- Use a collection system to retrieve data
  • 1. Retrieve data from structured sources (CSV)
  • 2. Retrieve data from unstructured sources
  • 3. Synthetic Data Generation
  • 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
- Use best practice considerations relating to data integrity structures
  • 1. Implement constraints
  • 2. Define primary keys for tables
  • 3. Perform table joins between parent/child tables
- Perform data discovery to identify what is needed from available datasets
  • 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 2. Query tables to assess data elements and statistics maintained by Snowflake
  • 3. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 4. Identify elements required for business goals using BI reports or SQL analysis
  • 5. Determine the level of data granularity required
- Prepare data and load into Snowflake
  • 1. Load files using Snowsight
  • 2. Load data from external/internal stages into a table
- Implement data processing solutions
  • 1. Use logging and monitoring solutions (auditing, data lineage)
  • 2. Cleanse, conform, and enrich data
  • 3. Respond to processing failures
  • 4. Automate and implement data pipelines (scheduling)
Topic 2: Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Present analytical results
  • 2. Snowsight dashboards
- Integrate with BI tools
  • 1. Other partner visualization tools
  • 2. Tableau integration
  • 3. Power BI integration
Topic 3: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. Common Table Expressions (CTEs)
  • 3. Window functions
  • 4. QUALIFY clauses
- Design data models
  • 1. Star schema design
  • 2. Snowflake schema design
  • 3. Data vault models
Topic 4: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Snowflake-specific analytical features
  • 2. Time-series analysis
  • 3. Aggregate functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q28-Q33):

28. Frage
You are tasked with identifying PII (Personally Identifiable Information) within several tables in your Snowflake data warehouse before granting access to a new analytics team. You have a database called 'CUSTOMER DATA with tables 'CUSTOMERS' , 'ADDRESSES' , and 'ORDERS'. Which of the following SQL queries, leveraging Snowflake's information schema, would be the most efficient and least intrusive method to discover potential PII columns, assuming you have a naming convention where PII columns often contain terms like 'EMAIL', 'PHONE', 'SSN', or 'NAME'?

Antwort: C

Begründung:
The correct answer is E. It leverages the 'information_schema.columns' view, which is a standard and efficient way to retrieve metadata in Snowflake. using 'table_schema = 'CUSTOMER_DATA" and 'table_name IN ('CUSTOMERS', 'ADDRESSES', 'ORDERS')' filters the results to the specific database and tables of interest, making it more efficient than scanning all columns in the account or using table_catalog'. ensures case-insensitive matching. Options A and D uses either wrong schema or wrong like. Option B and C uses account_usage, that can introduce delay to data availability since that is for billing and monitoring and also does not filter by table names


29. Frage
A Snowflake table 'SALES_DATA' contains a 'TRANSACTION_ID' (VARCHAR), 'AMOUNT (VARCHAR), and 'TRANSACTION DATE (VARCHAR) column. Some 'TRANSACTION_ID' values are alphanumeric, others are purely numeric. The 'AMOUNT' column sometimes contains currency symbols ('$', ' ') or commas, and 'TRANSACTION DATE' is in 'MM/DD/YYYY' format. You need to perform the following transformations: 1. Extract only numeric 'TRANSACTION ID's. 2. Convert "AMOUNT' to a numeric type for calculations, removing currency symbols and commas. 3. Convert 'TRANSACTION DATE to a DATE type. Which of the following SQL queries effectively accomplishes these data type transformations in Snowflake?

Antwort: B

Begründung:
Option E is the most comprehensive and robust solutiom - It uses 'REGEXP LIKE to filter out non-numeric and the CASE statement, which is important because 'TRANSACTION_lD's will have both numeric and alphanumeric values. - The 'AMOUNT' column correctly uses 'REGEXP_REPLACE and 'TRY_CAST to handle multiple currency symbols and converts the values to DECIMAL. -is used which handles incorrect data in DATE conversion and return NULL in case of invalid 'TRANSACTION_DATE. Option A is incorrect because IS_INTEGER is not a standard built-in Snowflake function. Option B can cause errors if the TRANSACTION_ID cannot be converted to INTEGER after being checked with REGEXP LIKE. Option C's CAST statements can cause errors if there's any data that cannot be correctly CAST.


30. Frage
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?

Antwort: C

Begründung:
Materialized views provide precomputed snapshots, enhancing query performance.


31. Frage
You are examining sales data for an e-commerce company in Snowflake. You notice a recurring dip in sales every Wednesday. The 'SALES' table contains columns: 'order_id', 'order date' , 'product_id', 'quantity', 'price'. Which of the following SQL queries and analytical techniques would be the MOST effective for diagnosing the reasons behind this weekly sales dip, assuming no external promotional activity always happens on Wednesdays?

Antwort: A,C,D,E

Begründung:
Options A, B, C, and E are the most relevant. Option A helps confirm the dip. Option B identifies product-specific patterns. Option C investigates a potential shipping-related cause. Option E explores potential software changes, while Option D focuses on a possible but less relevant cause of system maintenance.


32. Frage
Consider a table 'sales data' with columns 'product id', 'sale date', and 'revenue'. You need to calculate the cumulative revenue for each product over time, but only for the top 10 products by total revenue. What is the most efficient way to achieve this in Snowflake?

Antwort: E

Begründung:
Option B is the most efficient. and 'QUALIFY allow filtering and ranking within a single query, and efficiently calculates the cumulative revenue. Option A requires a join which can be less performant. Option C involves creating a temporary table which adds overhead. Option D calculates cumulative revenue for all products before filtering, which is unnecessary work. Option E could be considered, however APPROX TOP_N is approximate and the requirement asks for an exact calculation.


33. Frage
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